chore(reset): v2.0.0 — storico certificato Deribit mainnet, ripartenza pulita

Reset del progetto su fondamenta verificate dopo la scoperta che l'intera
libreria "validata OOS" era artefatto di feed contaminato (print fantasma del
feed Cerbero TESTNET + storico Binance/USDT).

- Storico ricostruito da Deribit MAINNET (ccxt pubblico, tokenless) e
  CERTIFICATO (certify_feed.py): BTC/ETH puliti su TUTTA la storia
  (mediana 2-6 bps vs Coinbase USD), integrita' OHLC + coerenza resample
  (maxΔ 0.00) + cross-venue OK. Alt esclusi (illiquidi/divergenti: LTC/DOGE
  50-82% barre flat; XRP/BNB non certificabili).
- Verdetto sul feed pulito: FADE / PAIRS / XS01 / TSM01 morti (ogni
  portafoglio Sharpe -2.3..-3.0, DD ~40%); solo SH01 e frammenti HONEST
  con segnale residuo, da ri-validare in isolamento.
- Cleanup "restart pulito": strategie, stack live (src/live, src/portfolio,
  runner/executor, yml, docker), ~100 script ricerca/gate, waste/games/
  portfolios, dati non certificati + cache e 60+ diari -> archiviati in Old/
  (preservati, non cancellati). Diario consolidato in un unico documento.
- Skeleton ricerca tenuto: Strategy ABC + indicatori + src/fractal +
  src/backtest/engine + load_data; tool dati certificati (rebuild_history,
  certify_feed, audit_feed, multi_source_check).
- Universo dati ATTIVO: solo BTC/ETH (5m/15m/1h); guardrail fisico
  (load_data su alt -> FileNotFoundError). Esecuzione DISABILITATA, conto flat.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Old/scripts/waste/W01_baseline.py

@@ -0,0 +1,110 @@
+"""Analisi baseline: distribuzione pattern frattali e prima strategia naive."""
+from __future__ import annotations
+
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from src.data.downloader import load_data
+from src.fractal.patterns import encode_candles, find_patterns, pattern_frequency
+from src.backtest.engine import run_backtest, BacktestResult
+
+print("=" * 60)
+print("  ANALISI BASELINE — BTC 1H")
+print("=" * 60)
+
+df = load_data("BTC", "1h")
+print(f"\nDati: {len(df)} candele [{df['datetime'].iloc[0]} → {df['datetime'].iloc[-1]}]")
+
+# 1. Distribuzione pattern
+print("\n--- DISTRIBUZIONE PATTERN (3-6 candele) ---")
+candle_types = encode_candles(df)
+unique, counts = np.unique(candle_types, return_counts=True)
+type_map = {-1: "DOWN", 0: "DOJI", 1: "UP"}
+for t, c in zip(unique, counts):
+    print(f"  {type_map[t]}: {c} ({c/len(df)*100:.1f}%)")
+
+patterns = find_patterns(df, min_len=3, max_len=6)
+freq = pattern_frequency(patterns)
+print(f"\nPattern unici: {len(freq)}")
+print(f"\nTop 20 pattern più frequenti:")
+print(freq.head(20).to_string(index=False))
+
+# 2. Analisi predittiva: dopo ogni pattern, il prezzo sale o scende?
+print("\n\n--- ANALISI PREDITTIVA PER PATTERN ---")
+print("Per ogni pattern: % volte che il prezzo sale nelle N candele successive")
+
+LOOKAHEAD = [1, 3, 6, 12, 24]
+top_patterns = freq.head(30)["pattern"].tolist()
+
+results = []
+for code in top_patterns:
+    matching = [p for p in patterns if p.code == code]
+    if len(matching) < 50:
+        continue
+
+    row = {"pattern": code, "count": len(matching)}
+    for ahead in LOOKAHEAD:
+        ups = 0
+        valid = 0
+        for p in matching:
+            future_idx = p.end_idx + ahead
+            if future_idx >= len(df):
+                continue
+            valid += 1
+            if df["close"].iloc[future_idx] > df["close"].iloc[p.end_idx - 1]:
+                ups += 1
+        if valid > 0:
+            row[f"up_{ahead}h"] = round(ups / valid * 100, 1)
+        else:
+            row[f"up_{ahead}h"] = None
+    results.append(row)
+
+pred_df = pd.DataFrame(results)
+print(pred_df.to_string(index=False))
+
+# 3. Strategia naive: compra quando il pattern più bullish si presenta
+print("\n\n--- STRATEGIA 1: PATTERN BULLISH NAIVE ---")
+# Trova pattern con up_24h > 55%
+bullish_patterns = pred_df[pred_df["up_24h"] > 55]["pattern"].tolist()
+bearish_patterns = pred_df[pred_df["up_24h"] < 45]["pattern"].tolist()
+print(f"Pattern bullish (>55% up in 24h): {len(bullish_patterns)}")
+print(f"Pattern bearish (<45% up in 24h): {len(bearish_patterns)}")
+
+# Genera segnali
+signals = pd.Series(0, index=df.index)
+all_patterns = find_patterns(df, min_len=3, max_len=6)
+for p in all_patterns:
+    if p.code in bullish_patterns:
+        signals.iloc[p.end_idx - 1] = 1
+    elif p.code in bearish_patterns:
+        if signals.iloc[p.end_idx - 1] == 0:
+            signals.iloc[p.end_idx - 1] = -1
+
+# Train/test split: 70/30
+split_idx = int(len(df) * 0.7)
+train_df = df.iloc[:split_idx].reset_index(drop=True)
+test_df = df.iloc[split_idx:].reset_index(drop=True)
+train_signals = signals.iloc[:split_idx].reset_index(drop=True)
+test_signals = signals.iloc[split_idx:].reset_index(drop=True)
+
+train_result = run_backtest(train_df, train_signals, initial_capital=1000, fee_pct=0.001)
+test_result = run_backtest(test_df, test_signals, initial_capital=1000, fee_pct=0.001)
+
+print("\nRISULTATI TRAIN (70%):")
+for k, v in train_result.summary().items():
+    print(f"  {k}: {v}")
+
+print("\nRISULTATI TEST (30%):")
+for k, v in test_result.summary().items():
+    print(f"  {k}: {v}")
+
+# 4. Buy & Hold come benchmark
+print("\n\n--- BENCHMARK: BUY & HOLD ---")
+bh_signals = pd.Series(0, index=test_df.index)
+bh_signals.iloc[0] = 1  # Compra al primo candle
+bh_result = run_backtest(test_df, bh_signals, initial_capital=1000, fee_pct=0.001, max_hold_candles=len(test_df))
+print("Buy & Hold (test period):")
+for k, v in bh_result.summary().items():
+    print(f"  {k}: {v}")